The RL agents for text-based games can be divided as text-based agents and KG-based agents based on the form of observations.
Compared with the text-based agents [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, which take the raw textual observations as input to build state representations, the KG-based agents construct the knowledge graph and leverage it as the additional input [8]}, [9]}.
By providing structural and historical information, the knowledge graph helps the agent to handle partial observability, reduce action space, and improve generalizability across games.
Based on how actions are selected, the RL agents can also be divided as parser-based agents, choice-based agents, and template-based agents.
The parser-based agents generate actions word by word, leading to a huge combinatorial action space [10]}.
The choice-based agents circumvent this challenge by assuming the access to a set of admissible actions at each game state [11]}.
The template-based agents achieve a trade-off between the huge action space and the assumption of admissible action set by introducing the template-based action space, where the agent selects first a template, and then a verb-object pair either individually [12]} or conditioned on the selected template [13]}.
In this work, we aim to improve the sample efficiency and reduce the action space through pre-training. Being agnostic about the form of observations and the action selecting methods, our work complements the existing RL agents.
